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Factor analysis model evaluation through likelihood cross-validation.

George J Knafl1, Margaret Grey

  • 1School of Nursing, Oregon Health and Science University, 3455 SW US Veterans Hospital Road, Portland, OR 97239, USA. knaflg@ohsu.edu

Statistical Methods in Medical Research
|May 9, 2007
PubMed
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This study presents a novel cross-validation method for evaluating survey scales in medical research. It objectively determines factor structure, aiding in the analysis of clinical trial data, particularly for adolescents with type 1 diabetes.

Area of Science:

  • Psychometrics
  • Biostatistics
  • Clinical Research Methodology

Background:

  • Medical research frequently employs survey instruments with multiple items aggregated into scales.
  • Evaluating the psychometric properties of these scales is crucial for study validity.
  • Existing methods for scale evaluation can be subjective or lack comprehensive approaches.

Purpose of the Study:

  • To introduce and demonstrate an objective, likelihood-based cross-validation approach for evaluating factor analysis models of survey scales.
  • To provide a systematic method for making key decisions in scale evaluation, including factor number and structure.
  • To enhance the reliability and validity of survey data in medical research settings.

Main Methods:

  • Development of a likelihood-based cross-validation framework.

Related Experiment Videos

  • Application to exploratory and confirmatory factor analysis models.
  • Objective decision-making regarding covariance structure, factor extraction, item allocation, and inter-scale dependence.

    • Main Results:

    • Demonstration of the approach using baseline data from three survey instruments.
    • Successful application in a clinical trial context involving adolescents with type 1 diabetes.
    • Objective criteria for evaluating scale structure and psychometric properties were established.

    Conclusions:

    • The presented cross-validation method offers an objective and robust approach to evaluating survey scales in medical research.
    • This method can improve the quality and interpretability of data from clinical trials and other health-related studies.
    • Facilitates more reliable measurement and analysis of patient-reported outcomes.